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• W2593246232 abstract "A Mobile Wireless Sensor Network (MWSN) is composed of mobile sensor nodes, whereby the life time of a sensor is specified by the life time of its battery. MWSN is used to measure a mobile object parameters then transmit the sensed data to center station called Sink Node (SN). Routing of the sensed data is a challenging issue since several parameters and restrictions should be managed carefully in MWSN. Although the sensor's power (e.g., sensor's battery level) is a critical issue, managing data transmission time is also a considerable subject especially for real time applications. Several routing protocols had been proposed for MWSN, however, each protocol considers a single type of awareness (such as; long life, delay time, total energy…). While some protocols consider the sensor's energy to maximize the network lifetime, others aim to find the shortest path to reach the destination. In this paper, a multi-aware query driven (MAQD) routing protocol will be proposed for MWSN based on a neuro-fuzzy inference system. MAQD considers four types of awareness; the long life of the sensor, delay time of data transmission, total cost of the network, and the shortest transmission path. Hence, based on the selected type of awareness, a fuzzy inference system selects the proper path for routing data. MAQD is a query driven protocol, accordingly, SN can collect data from some/all sensors by employing a request (REQ) message in which the awareness type is specified. MAQD is simulated and tested using OPNET 14.5 and compared with the latest MWSN routing protocols. Simulation results have shown that MAQD outperforms the selected competitors routing protocols (LEACH, ERTLD, RACE, SPIN, EAR2, DCBM, and Rumor routing) as it introduces the best data delivery with the minimum routing overheads in terms of time penalties and power consumed." @default.
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• W2593246232 date "2017-06-01" @default.
• W2593246232 modified "2023-10-14" @default.
• W2593246232 title "A Multi-Aware Query Driven (MAQD) routing protocol for mobile wireless sensor networks based on neuro-fuzzy inference" @default.
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• W2593246232 doi "https://doi.org/10.1016/j.jnca.2017.02.016" @default.
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